b17d53c0cd
In order to support tests running in eager mode we need to avoid unnecessary use of Sessions in tests. This moves to remove some of the uses of the `run` function in favor of `evaluate`. PiperOrigin-RevId: 223009795
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Simple benchmarks for reductions and their gradients."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import time
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import numpy as np
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from six.moves import range # pylint: disable=redefined-builtin
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from tensorflow.core.protobuf import config_pb2
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from tensorflow.python.client import session
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from tensorflow.python.eager import backprop
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from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gradients_impl
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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class ReduceBenchmarks(test.Benchmark):
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"""Benchmarks for reductions."""
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def _run(self, func, num_iters):
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# call func to maybe warm up the GPU
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func()
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start = time.time()
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for _ in range(num_iters):
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func()
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end = time.time()
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mean_us = (end - start) * 1e6 / num_iters
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self.report_benchmark(
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iters=num_iters,
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wall_time=mean_us,
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extras={"examples_per_sec": num_iters / (end - start)})
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def benchmark_reduce_sum_grad_eager(self):
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with context.eager_mode():
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tensor = array_ops.zeros([100, 1000])
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def fn():
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backprop.gradients_function(math_ops.reduce_sum, [0])(tensor)
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self._run(fn, 10000)
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def benchmark_reduce_sum_grad_eager_cpu(self):
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with context.eager_mode(), ops.device("/cpu:0"):
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tensor = array_ops.zeros([100, 1000])
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def fn():
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backprop.gradients_function(math_ops.reduce_sum, [0])(tensor)
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self._run(fn, 10000)
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def benchmark_reduce_sum_grad_graph(self):
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config = config_pb2.ConfigProto(
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graph_options=config_pb2.GraphOptions(
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optimizer_options=config_pb2.OptimizerOptions(
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opt_level=config_pb2.OptimizerOptions.L0)))
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with ops.Graph().as_default(), session.Session(config=config) as sess:
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tensor = constant_op.constant(np.zeros([100, 1000], dtype=np.float32))
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reduction = math_ops.reduce_sum(tensor)
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grad, = gradients_impl.gradients(reduction, tensor)
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def fn():
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self.evaluate(grad.op)
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self._run(fn, 10000)
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def benchmark_reduce_sum_grad_graph_cpu(self):
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config = config_pb2.ConfigProto(
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graph_options=config_pb2.GraphOptions(
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optimizer_options=config_pb2.OptimizerOptions(
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opt_level=config_pb2.OptimizerOptions.L0)))
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with ops.Graph().as_default(), session.Session(config=config) as sess:
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with ops.device("/cpu:0"):
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tensor = constant_op.constant(np.zeros([100, 1000], dtype=np.float32))
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reduction = math_ops.reduce_sum(tensor)
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grad, = gradients_impl.gradients(reduction, tensor)
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def fn():
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self.evaluate(grad.op)
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self._run(fn, 10000)
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if __name__ == "__main__":
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test.main()
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